Systems and methods for optimized delivery of targeted media

ABSTRACT

Systems and methods are disclosed for targeting of advertising content for a consumer product, by obtaining consumer demographic data, the consumer demographic data including a plurality of demographic attributes for each person; identifying a plurality of media slots; and obtaining program information for a respective identified program aired in each media slot among the plurality of media slots, the program information including viewing data of a plurality of viewing persons viewing the program and each viewing person being among the plurality of persons. The methods also include enriching the viewing data with the consumer demographic data; identifying a plurality of advertiser industries; enriching the product purchaser data with the consumer demographic data; calculating a relevance of each advertiser industry among the plurality of advertiser industries for each identified program based on demographic attributes of the product purchasers in each advertiser industry and demographic attributes of the viewing persons.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of priority to U.S. ProvisionalPatent Application No. 62/032,936, entitled “Systems and Methods forSell-Side TV Ad Optimization,” filed on Aug. 4, 2014, which isincorporated herein by reference in its entirety.

This application makes reference to U.S. non-provisional applicationSer. No. 13/209,346, entitled “Automatically Targeting Ads to TelevisionUsing Demographic Similarity,” filed Aug. 12, 2011, which isincorporated herein by reference in its entirety.

TECHNICAL FIELD

Various embodiments of the present disclosure relate generally toimproving methods for providing targeted, e.g., user-specific,advertising to users' television set-top boxes.

BACKGROUND

Television advertising remains the largest advertising category in theUnited States and has been the premium medium for advertising since the1950s. Television networks, broadcasters, and cable companies generateapproximately 75 billion dollars per year in revenue from ads insertedinto television program breaks.

Advertisers bid for placement in commercial breaks, and can optionallyspecify the television program, network, or hours during which theywould want their ad to run. In turn, television networks then insert thead based on the advertiser constraints. Ads are then embedded/insertedinto the video stream in commercial breaks.

However there are many questions to be answered for television networksabout exactly how to insert the ads. Television networks generally havefairly loose constraints about what ads they can insert where. Howshould they insert ads so as to maximize their yield, and perhaps theyield for their advertising clients also?

Previous work in ad relevance is most prevalent in online advertising(Hillard, et. al., 2010). Bing and Google utilize click through rate asa measure of relevance to balance revenue generation with userexperience. Ranking functions for search ads use click through ratemultiplied by price (Jansen, 2006). In contrast, there has been littlework on TV ad relevance (Hanssens, et. al., 2001; Johansson, 1979; Simonand Arndt, 1980; Jones, 1997; Vakratsas, et. al., 2004). Ewing (2013)used survey methods to measure television ad relevance from 2002 to2013. However this work was at a very high level and it did not go intonetworks, programs, or how to improve relevance. Zigmond, Dorai-Raj,Interian and Naverniouk (2009) used viewer tune-away behavior duringcommercial breaks as a proxy for relevance. However, none of theaforementioned studies have resulted in a suitable level ofindividual-specific targeting desired by television advertisers today.

The present disclosure is directed to overcoming one or more of theseabove-referenced challenges.

SUMMARY OF THE DISCLOSURE

According to certain aspects of the disclosure, methods are disclosedfor recommending television ad placement for multiple advertisers, onemethod comprising: identifying a plurality of media slots; obtaining,from a first server over a network, program information for a respectiveidentified program aired in each media slot among the plurality of mediaslots, the program information including viewing data of a plurality ofviewing persons viewing the program; identifying a plurality ofadvertiser industries; calculating a first relevance of each advertiserindustry among the plurality of advertiser industries for eachidentified program; associating an advertiser industry among theplurality of advertiser industries with each respective identifiedprogram; calculating a second relevance of the associated advertiserindustry for the respective identified program; and generatingrecommendations for an advertiser among the plurality of advertiserindustries based on the calculated first relevance of each advertiserindustry and the calculated second relevance of the associatedadvertiser industry.

According to certain aspects of the disclosure, systems are disclosedfor recommending television ad placement for multiple advertisers, onesystem comprising: a first server providing program information for arespective identified program aired in each media slot among a pluralityof media slots over a network, the program information including viewingdata of a plurality of viewing persons viewing the program; and anadvertising targeting controller configured to: obtain the programinformation; identify the plurality of media slots; identify a pluralityof advertiser industries; calculate a first relevance of each advertiserindustry among the plurality of advertiser industries for eachidentified program; associate an advertiser industry among the pluralityof advertiser industries with each respective identified program;calculate a second relevance of the associated advertiser industry forthe respective identified program; and generate recommendations for atarget advertiser industry among the plurality of advertiser industriesbased on the calculated first relevance of each advertiser industry andthe calculated second relevance of the associated advertiser industry.

According to certain aspects of the disclosure, non-transitory computerreadable media are disclosed storing a program causing a computer toexecute a method of recommending television ad placement for multipleadvertisers. One method comprises: identifying a plurality of mediaslots; obtaining, from a first server over a network, programinformation for a respective identified program aired in each media slotamong the plurality of media slots, the program information includingviewing data of a plurality of viewing persons viewing the program;identifying a plurality of advertiser industries; calculating a firstrelevance of each advertiser industry among the plurality of advertiserindustries for each identified program; associating an advertiserindustry among the plurality of advertiser industries with eachrespective identified program; calculating a second relevance of theassociated advertiser industry for the respective identified program;and generating recommendations for a target advertiser industry amongthe plurality of advertiser industries based on the calculated firstrelevance of each advertiser industry and the calculated secondrelevance of the associated advertiser industry.

Additional objects and advantages of the disclosed embodiments will beset forth in part in the description that follows, and in part will beapparent from the description, or may be learned by practice of thedisclosed embodiments. The objects and advantages of the disclosedembodiments will be realized and attained by means of the elements andcombinations particularly pointed out in the appended claims. As will beapparent from the embodiments below, an advantage to the disclosedsystems and methods is that multiple parties may fully utilize theirdata without allowing others to have direct access to raw data. Thedisclosed systems and methods discussed below may allow advertisers tounderstand users' online behaviors through the indirect use of raw dataand may maintain privacy of the users and the data.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive of the disclosed embodiments, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate various exemplary embodiments andtogether with the description, serve to explain the principles of thedisclosed embodiments.

FIG. 1 depicts an exemplary analytics environment and an exemplarysystem infrastructure for modeling and detailed targeting of televisionmedia, according to exemplary embodiments of the present disclosure.

FIG. 2 depicts a flowchart for high dimensional set top box targeting,according to exemplary embodiments of the present disclosure.

FIG. 3 depicts a flowchart for estimating ad relevance across a range ofindustries, according to exemplary embodiments of the presentdisclosure.

FIG. 4 depicts a report showing network relevance, according toexemplary embodiments of the present disclosure.

FIG. 5 depicts a report showing network relevance, according toexemplary embodiments of the present disclosure.

FIG. 6 depicts a flowchart for sell-side optimization, according toexemplary embodiments of the present disclosure.

FIG. 7 depicts a flowchart for advertiser yield maximization, accordingto exemplary embodiments of the present disclosure.

FIGS. 8 and 9 depict reports providing schedule improvements to increasead relevance, according to exemplary embodiments of the presentdisclosure.

FIGS. 10A and 10B depict an advertising schedule with visual shading,according to exemplary embodiments of the present disclosure.

FIG. 11 depicts a report providing advertisers to contact, according toexemplary embodiments of the present disclosure.

FIG. 12 depicts a report providing possible ad insertions by tratiodifference, according to exemplary embodiments of the presentdisclosure.

FIG. 13 depicts a report providing ad relevance for one particularadvertiser, according to exemplary embodiments of the presentdisclosure.

FIG. 14 depicts a sell-side graphical user interface (GUI), according toexemplary embodiments of the present disclosure.

FIGS. 15-17 depict a GUI to generate an advertiser contact list,according to exemplary embodiments of the present disclosure.

FIG. 18 depicts a report providing the largest gains in ad relevance,according to exemplary embodiments of the present disclosure.

FIG. 19 depicts a report providing the best media for an advertiser,according to exemplary embodiments of the present disclosure.

DETAILED DESCRIPTION OF THE DRAWINGS

FIG. 15. “Contact list GUI”: The user selects Market, Network (Station),Day, Hour and/or Program name (if applicable). The system then presentsa list of advertisers to contact who would be likely to want to placeads in a commercial break for that program/time/network. The inventorycould be specified in more detail—for example, pod group number (thecommercial break number, e.g. 1 is the first commercial break) and podposition (the order in which the ad appears in the commercial break,e.g. 2 means it would be the second ad to appear in the break) couldalso be selected. In 2015 media is often not bought with pod position aspart of the negotiations, however it has sometimes been added as arequest when buying media.

This figure shows the “Contact list for MTV primetime weekend”: MTV topadvertisers to contact would be streaming music companies, followed byColleges. The audience for MTV tends to be young adults, and theseproducts are in great demand from young adults. It is a little ironicthat music service companies such as Rdio, Beats, Apple, etc., all showup on a television network that is “Music Television,” but this is whathappens—these music services tend to be favored by young adults which iswhy they sort to the top. Technical colleges also show up as advertiserswho would be interested in the inventory.

FIG. 16 shows an example “Contact-list for E! daytime on Wed”: E!Entertainment Wednesday 11 am top advertisers to contact would be secondhand clothing companies such as Zulily, Care, Joss and Main; interiordecoration companies such as Art.com, followed by cosmetics. Theaudience for E! tends to be budget-conscious young women with youngfamilies, and so these industries showing up as top prospects for E!Daytime makes a lot of sense.

FIG. 17 shows an example “Contact list for Fox News”: Fox News Saturday7 pm: Top ads to insert would be AARP, Mutual of Omaha and PhysiciansMutual Life insurance, followed by a variety of luxury cars. The viewersof Fox News on Saturdays at 7 pm tend to be working age, elderly andhigher income people, which is why life insurance and luxury autos arerelevant ads—these advertising categories are purchased by peoplematching the demographics for Fox News. The contact list of agencies whoare responsible for each advertiser is shown at right.

DETAILED DESCRIPTION OF EMBODIMENTS

Various embodiments of the present disclosure relate generally toimproving methods for providing targeted advertising to televisionset-top boxes. Specifically, embodiments include creating a demographicprofile of consumers of a particular product, along with a demographicprofile of a population viewing a particular TV show or a demographicprofile of an individual using a set-top box. Thereafter, targetedadvertising may be inserted into media by comparing the demographicprofile of consumers of particular products with the other demographicprofiles. The embodiments of this disclosure uniquely allow forproviding specified targeted advertising at an individual consumer levelusing set-top boxes.

Thus, the present disclosure is directed to a system for measuring adrelevance that may be used as part of an optimization system to improvenetwork and advertiser outcomes, both in determining what inventory isavailable, and where to place ads in order to overcome one or more ofthe above-referenced challenges. Aspects of the present disclosure, asdescribed herein, relate to systems and methods for automated televisionad targeting using set top box data. Aspects of the present disclosureinvolve selecting a segment of TV media to purchase to insert an ad,such that advertiser value per dollar is maximized.

Various examples of the present disclosure will now be described. Thefollowing description provides specific details for a thoroughunderstanding and enabling description of these examples. One skilled inthe relevant art will understand, however, that the present disclosuremay be practiced without many of these details. Likewise, one skilled inthe relevant art will also understand that the present disclosure mayinclude many other related features not described in detail herein.Additionally, some understood structures or functions may not be shownor described in detail below, so as to avoid unnecessarily obscuring therelevant description.

The terminology used below may be interpreted in its broadest reasonablemanner, even though it is being used in conjunction with a detaileddescription of certain specific examples of the present disclosure.Indeed, certain terms may even be emphasized below; however, anyterminology intended to be interpreted in any restricted manner will beovertly and specifically defined as such in this Detailed Descriptionsection.

The systems and method of the present disclosure allow for automatedtelevision ad targeting using set top box data.

I. System Architecture

Any suitable system infrastructure may be put into place to receivemedia related data to develop a model for targeted advertising fortelevision media. FIG. 1 and the following discussion provide a brief,general description of a suitable computing environment in which thepresent disclosure may be implemented. In one embodiment, any of thedisclosed systems, methods, and/or graphical user interfaces may beexecuted by or implemented by a computing system consistent with orsimilar to that depicted in FIG. 1, which may operate according to thedescriptions of U.S. patent application Ser. No. 13/209,346, filed Aug.12, 2011, the disclosure of which is hereby incorporated herein byreference. Although not required, aspects of the present disclosure aredescribed in the context of computer-executable instructions, such asroutines executed by a data processing device, e.g., a server computer,wireless device, and/or personal computer. Those skilled in the relevantart will appreciate that aspects of the present disclosure can bepracticed with other communications, data processing, or computer systemconfigurations, including: Internet appliances, hand-held devices(including personal digital assistants (“PDAs”)), wearable computers,all manner of cellular or mobile phones (including Voice over IP(“VoIP”) phones), dumb terminals, media players, gaming devices,multi-processor systems, microprocessor-based or programmable consumerelectronics, set-top boxes, network PCs, mini-computers, mainframecomputers, and the like. Indeed, the terms “computer,” “server,” and thelike, are generally used interchangeably herein, and refer to any of theabove devices and systems, as well as any data processor.

Aspects of the present disclosure may be embodied in a special purposecomputer and/or data processor that is specifically programmed,configured, and/or constructed to perform one or more of thecomputer-executable instructions explained in detail herein. Whileaspects of the present disclosure, such as certain functions, aredescribed as being performed exclusively on a single device, the presentdisclosure may also be practiced in distributed environments wherefunctions or modules are shared among disparate processing devices,which are linked through a communications network, such as a Local AreaNetwork (“LAN”), Wide Area Network (“WAN”), and/or the Internet. In adistributed computing environment, program modules may be located inboth local and remote memory storage devices.

Aspects of the present disclosure may be stored and/or distributed onnon-transitory computer-readable media, including magnetically oroptically readable computer discs, hard-wired or preprogrammed chips(e.g., EEPROM semiconductor chips), nanotechnology memory, biologicalmemory, or other data storage media. Alternatively, computer implementedinstructions, data structures, screen displays, and other data underaspects of the present disclosure may be distributed over the Internetand/or over other networks (including wireless networks), on apropagated signal on a propagation medium (e.g., an electromagneticwave(s), a sound wave, etc.) over a period of time, and/or they may beprovided on any analog or digital network (packet switched, circuitswitched, or other scheme).

II. Defining Calculation of Television Ad Relevance

One method of calculating ad relevance according to exemplaryembodiments of the present disclosure, as illustrated in FIG. 2, mayinclude obtaining a set of persons who have proven their interest bypurchasing the service or product in question (step 210); obtaining aset of persons who view the ad (step 220); and calculating thecorrelation coefficient between the viewers and the purchasers (step230).

The resulting ad relevance measure may be referred to as the “tratio.”Tratio may be a parameter bounded between −1 and 1. Detailed informationon tratio calculations are provided in the Glossary section.

Ad relevance may be estimated across a range of industries. FIG. 3illustrates a method for such estimation according to exemplaryembodiments of the present disclosure as follows. As an initial matter,the method of FIG. 3 may optionally include obtaining commerciallyavailable consumer demographics (step 305).

1. Define Advertiser Industries: Define a set of industries {A} which isa set of advertiser-industries who are advertising on television. (step310) The industries may include, for example, “High Income Credit Card,”“Power Tools,” “Home Furnishings,” “Life Insurance,” “Jewelry,”“Education Online,” “Luxury Autos”, “Pickup Trucks,” and many others. Anexample of defined industries is shown in Table 1, below, and someexample mappings between advertisers and the industries are shown inTable 2, below. Defining industries can be done by mapping a Nielsenrecorded classification to an industry. The Glossary includes two moredetailed examples of industries and their definitions.

2. Extract a set of television airings for the industry A M_(i)(A) (step315). There are 750 million airings in the United States over 3 years ofUS television airings. These 750 million airings may be sampled forefficiency reasons to a smaller number of airings, sampled randomly(e.g. 2.5 million airings). Historical U.S. television airings may betracked by Nielsen, and Nielsen may provide its own naming convention todescribe the advertiser. A mapping table may be used to map theseadvertiser names to appropriate industries. The airings can be sampled,for example, by sampling where the modulus of a unique and randomlyassigned airingID is equal to a particular value.

TABLE 1 Example Industries Job ID Advertiser Name 1 Charity 2 DiabeticHealth insurance 3 Diet 4 Dental Insurance 5 Home Furnishings 6Investment Services 7 Life Insurance 8 Music 9 Power tools 10 SUVs 11 Trucks 12 Education online 52 Diabetes Health insurance 53 Luxury auto 54Truck Pickup 55 PMIC Brand 56 High Income Credit Card 57 Senior LifeInsurance 58 DIY investment 59 Exercise Equipment 60 FitnessProgram/Club 61 Term 62 life insurance investment 63 Cosmetics 64Teenage extra-curricular activities 65 Technical colleges 66 Children'slearning program 67 Jewelry 68 Interior Decoration

TABLE 2 Example Advertiser classifications into industries AdvertiserName Identifier Name Nielsen Prod. Hierarchy Charity CharitableOrganization Product Category Dental Insurance Dental Services ProductCategory Investment Services AMERIPRISE FINANCIAL INC SubsidiaryInvestment Services CHARLES SCHWAB & CO INC Subsidiary InvestmentServices E TRADE SECURITIES INC Subsidiary Investment Services FIDELITYDISTRIBUTORS CORP Subsidiary Investment Services Financial-InvestmentServices Product Category Investment Services GAIN CAPITAL GROUP LLCSubsidiary Investment Services INTERACTIVE BROKERS LLC SubsidiaryInvestment Services SCOTTRADE INC Subsidiary Investment ServicesSHAREBUILDER CORP Subsidiary Investment Services SPEEDTRADER.COM INCSubsidiary Investment Services TD AMERITRADE INC Subsidiary InvestmentServices TRADESTATION SECURITIES INC Subsidiary Investment ServicesUNITED SVCS AUTOMOBILE ASSN Subsidiary Power tools Power Tools-AccessProduct Category

3. Obtain a set of product purchasers who have purchased a product inindustry A, P(A) (step 320). For example, embodiments of the presentdisclosure may use data from 6.8 million persons who had previouslybought products across 25 advertiser industries.

4. Enrich the set of product purchasers with consumer demographics (fromstep 305 and 325). For example, 3,500 or more demographic elements maybe used in one embodiment.

5. Obtain data on the viewing audience of television media, which maygenerally include programs, but may include any contiguous set of video(step 330). Enrich the audience data with the consumer demographics(step 335).

6. Summarize the audience of programs by calculating an aggregateddemographic profile M_(j,d,v)(A) (step 350) using set top box personviewing data q∈Q that may be captured from televisions comprising aviewing event for person on media M_(i)(A).

7. Optionally perform a “lookalike expansion” on the set of productpurchasers by matching the demographic profiles of the purchasers withdemographic profiles of the viewers to find the best matching N>=1persons in the television viewing population (step 345) as P(A)_(d,v).This will find persons in the set top box TV viewing population who looksimilar to those historical purchasers based on their demographiccharacteristics. This may improve the buyer data quality, which can helpto provide for good fidelity matching between the vector profiles.Look-a-like expansion does not need to be used if there are sufficientproduct purchasers.

8. Calculate a relevance score, between the product purchasersP(A)_(d,v) and each ad airing M_(j,d,v)(A). One calculation that can beused is a correlation coefficient. Often this relevance score isreferred to as a tratio (step 355).

Exemplary embodiments of the present disclosure may further includeusing demographic enrichment of the viewer and purchaser data (steps 325and 335) to possibly report on the demographic d and value v for anyperson p among the viewer and purchaser data.

III. Basic Television Relevance Reports

After defining industries and calculating relevance for ad airingswithin each industry according to exemplary embodiments of the presentdisclosure, exemplary embodiments of the present disclosure may generatea variety of reports.

A. Report 1: Most Relevant Ads

A Report showing Relevance by ad-program can be generated from a reportby showing the following columns: (Advertising-Industry, TV-program,tratio)

An example of this report is shown in Table 3 below. Table 3 shows thatcareer education ads on “MTV-Jersey Shore SSN4 Reunion” had the highestdegree of audience match. It also shows that diet industry purchasersmatch the audience for Food Network programs such as “FOOD-Fat Chef” and“FOOD—Chopped.”

TABLE 3 Most relevant programming placement for a selection ofindustries Relevance Ad Program Score Career Education MTV-JERSEY SHORESSN4 REUNION 0.616 Career Education MTV-JERSEY SHORE SSN 5 0.606 CareerEducation MTV-JERSEY SHORE SSN 4 0.605 Career Education MTV-JERSEY SHORE2 0.605 Career Education MTV-JERSEY SHORE SSN 6 0.605 Career EducationMTV-JERSEY SHORE SSN4 REU REC 0.604 Career Education SYN-PUNK'D AT 0.604Career Education MTV-JERSEY SHORE 2B 0.602 Career Education MTV-JERSEYSHORE 0.601 Career Education VH1-LOVE AND HIP HOP 2 0.589 CharityFOXB-IMUS IN THE MORNING 0.514 Charity WILD-PACK 0.500 CharityESP2-TENNIS: US OPEN SRS MEN L 0.497 Charity RFD-CROOK & CHASE 0.490Charity MSNB-POLITICS NATION 0.472 Charity ETV-GRAN CINE FRI 0.470Charity ETV-GRAN CINE THURS 0.468 Charity GRN-YELLOWSTONE: BATTLE FOR0.467 LI Charity FOXB-VARNEY & COMPANY 0.464 Charity FOXB-IMUS MUSICSPECIAL 0.463 Cosmetics BRAV-FASHION HUNTERS 0.515 Cosmetics STYL-HOTLISTINGS MIAMI 0.485 Cosmetics BRAV-DONT BE TARDY 0.484 CosmeticsSTYL-E! NEWS 0.478 Cosmetics STYL-WEDDINGS FROM HELL 0.478 CosmeticsBRAV-RING LEADER, THE 0.477 Cosmetics BRAV-K GRIFFIN: MY LIFE D-LIST0.476 Cosmetics BRAV-LIFE AFTER TOP CHEF 0.475 Cosmetics STYL-FACE, THE0.475 Cosmetics LIFE-ON ROAD AUSTIN & SANTINO 0.472 Diet FOOD-FAT CHEF0.454 Diet STYL-CLEAN HOUSE 0.442 Diet FOOD-CHOPPED 0.434 DietSTYL-PROJECT RUNWAY 0.425 Diet STYL-HOW DO I LOOK 0.420 DietFOOD-RACHAEL VS. GUY 0.419 Diet FOOD-CUPCAKE WARS 0.418 Diet DFH-DR. G:AMERICAS MOST SHOC 0.414 Diet FOOD-ACE OF CAKES 0.413 Diet TLC-SAY YESTO THE DRESS: ATL 0.410

B. Report 2: Relevance by Industry

Exemplary embodiments of the present disclosure may include generating areport showing overall relevance trends in the TV industry. A report onoverall relevance and pricing trends in the TV industry as a whole canbe defined with showing the timeseries of (Date, AdvertisingIndustry,tratio, CPM)

An example of this report is shown in Table 4, below. The exemplaryreport in Table 4 may show relevance by industry, along with priceinformation. Price enables the calculation of the value per dollar (orapproximate cost per buyer) from advertising in each industry bycalculating CPM/tratio. Such a report may show, for example, that it isexpensive to go after luxury auto buyers, but less expensive to reachcosmetics or fitness program customers. CPM30 is defined as 1000multiplied by the spot cost divided by impressions, and then scaled toan equivalent 30 second ad. The calculation is provided in the Glossary.

TABLE 4 Ad Relevance and Prices by Industry Impressions Cost30 CPM 30T-Ratio T-CPM30 per Airing per Airing Luxury auto $12.94 0.229 $56.47447,697 $5,792 DIY investment $11.78 0.212 $55.62 402,442 $4,740 TruckPickup $11.13 0.196 $56.86 484,196 $5,391 SUVs $10.16 0.153 $66.43540,640 $5,494 Investment Services $9.12 0.222 $41.01 382,821 $3,491Power tools $8.14 0.231 $35.25 394,389 $3,209 Term $7.97 0.227 $35.13306,677 $2,445 Charity $7.41 0.121 $61.09 544,432 $4,034 High IncomeCredit Card $7.23 0.178 $40.67 321,503 $2,323 Life Insurance $6.95 0.256$27.12 291,572 $2,027 Fitness Program/Club $6.32 0.267 $23.69 326,092$2,062 Interior Decoration $6.17 0.179 $34.51 310,786 $1,918 Cosmetics$6.15 0.127 $48.53 469,170 $2,885 Diet $5.73 0.133 $43.09 306,175 $1,755Technical colleges $5.71 0.237 $24.11 323,743 $1,848 Home Furnishings$5.17 0.120 $43.19 301,218 $1,559

C. Report 3: Relevance by Network

Exemplary embodiments of the present disclosure may generate a reportshowing network relevance according to date, network and tratio, withthe columns below: (Date, Network, tratio)

An example of this report is shown in FIGS. 4 and 5. According to theseexemplary reports, it may be concluded that TV relevance as a whole hasincreased between 2010 and 2013 by about 0.5% per year. In addition,these exemplary reports suggest that in 2013 the networks with the mostrelevant ads were: MTV, GOLTV, SPD, CNBC, MILI, ESQR, MTV2, FOXB andGOLF (FIG. 5).

In FIG. 5, tratio was converted into a “5 star rating” showingeffectively the percentile of tratio for each network. A score of 5 inFIG. 5 indicates that the tratio for the network was in the top 20^(th)percentile of all networks for that year, and a score of 1 means it wasin the lowest 20^(th) percentile. More details about “starratings”—which are used to provide a more human-readable version ofmetrics in these reports—is provided in the Glossary.

FIG. 4 indicates that of the broadcast stations in 2013, CW had the mostrelevant advertising, and Fox had the next most relevant advertising.FIG. 4 indicates that of the broadcast stations in 2013, CBS had theleast relevant advertising.

Also based on the report in FIG. 4, it may be noted that approximately40% of television ads have a relevance that is worse than random.Performance worse than random may be defined as reaching fewer productpurchasers than might be reached if an ad were targeted randomly inplacements on television.

In FIGS. 4 and 5, the ad relevance of each advertiser may be indicatedby providing tratios alone or in combination with additional visualinformation. The additional visual information may include, for example,coloring or shading of grid cells.

D. Report 4: Least Relevant Ads

Exemplary embodiments of the present disclosure may generate a reportshowing the least relevant ads amongst a collection of media. Thisreport can be generated by showing the following columns: (Industry,TV-program, tratio)

An example of this report is shown in Table 5. The report in indicatesthat (amongst other insights) (a) the most irrelevant ad for the luxuryauto industry was TNT's “Charmed.” T(b) The most relevant ad in thepower tools industry was on Military Channel's “Quest for SunkenWarships.”, (c) The most irrelevant ad for power tools ads were Women'sEntertainment Network's “I Do Over” and “My Fair Wedding.”

TABLE 5 Examples of Most and Least Relevant Ads Most Relevant LeastRelevant Industry Programs tratio Programs tratio Luxury auto HDNET-AUTOTRADER 0.546 TNT-CHARMED −0.473 Luxury auto HDNET-AMERICAN 0.527 TNT-−0.419 ICON - THE HOT R SUPERNATURAL Luxury auto GOLF-GOLF IN 0.522TV1-HUGHLEYS −0.419 AMERICA Luxury auto ESP2-PTI SPECIAL 0.517STYL-AMERICAS −0.413 NEXT TOP MODEL Luxury auto ESP2-BOXING SERIES L0.513 SYFY-URBAN −0.390 LEGENDS Luxury auto GOLF-BIG BREAK XVI: 0.510TV1-AMEN −0.380 IRELAND Luxury auto GOLF-USGA 0.506 TRAV-DEAD −0.373FILES Luxury auto HDNET-MOTORWEEK 0.505 TV1-DIVORCE −0.370 COURT Luxuryauto ESPN-COLLEGE 0.502 TV1-MOVIE −0.368 FOOTBALL PRIME L Luxury autoGOLF-HANEY 0.498 BET-MY BLACK −0.351 PROJECT IS BEAUTIFUL 2 Power toolsMILI-QUEST FOR 0.661 WE-I DO OVER −0.692 SUNKEN WARSHIPS Power toolsMILI-WINGS OVER 0.659 WE-MY FAIR −0.674 VIETNAM WEDDING Power toolsMILI-WINGS OF THE 0.654 WE-SINBAD: ITS −0.647 LUFTWAFFE JUST FAMILYPower tools MILI-COMBAT TECH 0.642 OXYG-REAL HW −0.645 ATLANTA Powertools MILI-CARRIER - 0.639 WE-BRAXTON −0.639 FORTRESS AT SEA FAMILYVALUES Power tools MILI-NAZIS: OCCULT 0.638 WE-PLATINUM −0.635CONSPIRACY WEDDINGS Power tools MILI-DECISIONS 0.635 BET-WENDY −0.599SHOOK THE WORLD WILLIAMS SHOW, THE Power tools MILI-ULTIMATE 0.632OXYG-BEST INK −0.583 GUIDE: PLANES Power tools MILI-ANATOMY OF 0.630OXYG-TORI & −0.580 DEAN HOME SW HLYWD Power tools MILI-WORLD AT WAR0.627 OXYG-AMERICAS −0.578 NXT TOP MODEL

IV. Sell-Side Optimizer

Exemplary embodiments of the present disclosure may provide a sell-sideoptimizer that may enable TV networks to increase ad relevance andauction density. An exemplary method and algorithm for a sell-sideoptimizer is illustrated in the flowchart of FIG. 6. In order to providesuch a sell-side optimizer, exemplary embodiments of the presentdisclosure may score every possible ad insertion defined across a set ofindustries A against every piece of inventory M_(i):

In one embodiment, media M_(i) can be defined as

M _(i)∈DateTime×S×G  [Equation 1]

where DateTime is a 30 minute time period during a broadcast week, S isTelevision Network, and G is geography, either DMA or National.

Equation 1 creates a Cartesian product of date-time, station, andgeography for the week under consideration (step 610). A TV schedulelookup is then performed to find the appropriate program that would beairing during a given week, thereby associating a Program(M_(i)) foreach M_(i) (step 620), scoring each media asset M_(i) against everypossible advertiser industry A_(j) that could be inserted for that media(step 630), accessing or calculating the current ads that are runningwith each media asset M_(i) (step 640), calculating the relevance ofeach current ad (step 650), and generating recommendations for theadvertiser based on calculated relevance (step 660).

In another embodiment, media M_(i) can be defined as

M _(i) ∈S×P×D×H×T×G×POD×POS×L  [Equation 2-1]

where S is Station, P is Program, D is Day-Of-Week, H is Hour-Of-Day, Tis Calendar-Time, G is Geography, POD is the Ad-Pod, POS is thePod-Position, and L is Media-Length. Stations may include Broadcast andCable stations and may be generally identified by their call-letters,such as KIRO and CNN. Geography may include National, Direct MarketAssociation Areas, such as Miami, Fla. and Cable Zones, such as ComcastMiami Beach. An “Ad Pod” may be a term used to reference a set ofadvertisements that run contiguously in time during the commercial breakfor a TV program. “Pod position” may be a term used to reference thesequential order of the ad within its pod. “Media Length” may be a termused to reference the duration of the time segment in seconds—common adlengths include 30, 15 and 60 second spots, where DateTime is a 30minute time period during a broadcast week, S is Television Network, andG is geography, either DMA or National. Once again, advertiserindustries can then be scored against the above media.

The use of “industries” rather than individual advertisers by exemplaryembodiments of the present disclosure may provide one or moreadvantages. First, doing so may quantize the space of advertisers, andthereby reduce the cardinality of the optimization problem, so insteadof 100,000 advertisers, the exemplary embodiments of the presentdisclosure may be able to calculate improved ad allocations using onlyseveral thousand industries. Also, data may not be available for alladvertisers who are part of the pool being analyzed by the system.Therefore, by using industries, data for advertiser-industries ingeneral can be used to help inform how ads should be allocated forparticular advertisers, even in the purchaser data for those advertisersisn't available. If data for individual advertisers is available, thenexemplary embodiments of the present disclosure, of course, may performoptimization with each individual advertiser's own data. However thedefinition of industries makes it possible to meaningfully optimizeadvertisers with or without individual data.

Exemplary embodiments of the present disclosure may include accessing orcalculating the current ads that are running with each media asset M_(i)in one or more ways. For example, exemplary embodiments of the presentdisclosure may include calculating the most frequent ad based onhistorical placements:

CurrentAd(M _(i))=MostFreqAd(M _(i))=A _(j):max occ(A _(j))  [Equation2]

as the most frequent historical ad inserted into this media M_(i). Thiscan be accomplished by counting the most frequent ad counting the numberof occurrences of each ad A_(j) in past airings of media M_(i).

Alternatively, exemplary embodiments of the present disclosure mayinclude setting CurrenAd(M_(i)) to equal the ad which is currentlyplanned to run in media placement M_(i) based on known advertiserupfront and scatter purchases. This ad can be determined by linking thesystem to sales or inventory tracking systems which have information onwhich advertisers have bought particular placements.

Exemplary embodiments of the present disclosure may include providingone or more Sell-Side Optimizer decision support reports based oncalculated relevance of each ad placement. Several example reports aredescribed next:

Sell-Side Optimizer Functions

Some of the functions which Sell-Side Optimizer is able to compute aredescribed below. The functions can be exposed to a user via a GraphicalUser Interface (GUI), via computer reports, via graphical datavisualizations, via XML or other data technologies. We will show anexample Graphical User Interface implementation that supports some ofthe functions described below following this section.

A. Most Relevant Inventory for an Advertiser

A list of recommended media by sorting inventory in order of tratio inorder to provide guidance to an advertiser on how to reach the mostbuyers per impression, according to the equation:

M _(i):max_(N) tratio(A _(j) ,M _(i))  [Equation 3]

Alternatively, exemplary embodiments of the present disclosure mayinclude providing a list of recommended media by sorting inventory inorder of tratio within particular CPM or cost thresholds in order torecommend media that is most relevant for the advertiser, according tothe equation:

M _(i):max_(N) tratio(A _(j) ,M _(i)) where CPM(M _(i))<C and Cost(M_(i))<Cost   [Equation 4]

B. Most Economical Inventory for an Advertiser

Exemplary embodiments of the present disclosure may include reportswhich provide a list of recommended media by sorting inventory in orderof the cost per targeted impression or tCPM in order to provide guidanceto an advertiser on the most cost effective media for their ad,according to the equation:

$\begin{matrix}{{M_{i}\text{:}\mspace{14mu} {\min_{N}{tCP{M\left( {A_{i},M_{i}} \right)}}}} = \frac{CP{M\left( {A_{j},M_{i}} \right)}}{{tratio}\left( {A_{j},M_{i}} \right)}} & \left\lbrack {{Equation}\mspace{14mu} 5} \right\rbrack\end{matrix}$

C. Agencies to Contact to Sell Inventory

Television Broadcasters or Publishers typically have to sell all oftheir inventory, and as an air date approaches, it becomes increasinglyimportant to find a buyer. If a buyer cannot be found, the publisherwill often give away the inventory in the form of bonus or in the formof a public service announcements. In such circumstances, publishers maywant to know which advertisers to contact, in order of likelihood ofpurchase, to monetize their inventory.

Exemplary embodiments of the present disclosure may provide a list ofrecommended advertisers to contact by ordering the top advertiserindustries for each media, according to the equation:

A _(j):max tratio(A _(j) ,M _(i))  [Equation 6]

In addition to helping to connect to buyers, it may be in thepublisher's interest to have more bidders on their market interested intheir inventory. Auction theory suggests that as the number of biddersincrease, so should the average price for the goods under auction.Therefore, increasing awareness of desirable media with potential buyersmay increase auction density. FIGS. 15, 16 and 17 show examples of agraphical user interface which shows the advertisers who are mostrelevant for a particular television spot.

D. Inventory that Will be Higher Performing than Another Network's

Publishers are in competition with other publishers, and may desire tomake the case to an advertiser that the advertiser should buy thepublisher's inventory rather than someone else's. Exemplary embodimentsof the present disclosure may include providing relevance scores inorder to enable a publisher to compare their inventory to programs onother networks in order to show which of their inventory are bettertargeted or have better value per dollar. These may allow the publisherto provide comparisons which are customized for each advertiser'sindustry.

E. Substitutes for Unavailable Inventory

Similarly, exemplary embodiments of the present disclosure may includeproviding relevance scores in order to enable a publisher to comparetheir available inventory to a particular program that is unavailable inorder to recommend available ad placements which are as good or betterthan the requested ad placement, both in price and targeting, accordingto the equations:

M _(i):max_(N) tratio(A _(j) ,M _(i))≥tratio(A _(j) ,M_(k))∀k  [Equation 7]

M _(i):min_(N) tCPM(A _(j) ,M _(i))≤tCPM(A _(j) ,M _(k))∀k  [Equation 8]

F. Maximum Increases in Relevance

A publisher may have flexibility when scheduling each ad. Advertisersmay buy ad packages in rotations, which represent time ranges when eachad is to be aired. Accordingly the publisher may be able to optimize adplacement within those rotations. Exemplary embodiments of the presentdisclosure may include providing a list of ad insertions that wouldresult in the greatest increase in overall relevance, according to thefollowing equation:

A _(j) ,M _(i):max(tratio(A _(j) ,M _(i))−tratio(MostFreqAd(M _(i)),M_(i)))  [Equation 9]

V. Yield Maximization: Advertiser Pricing

Exemplary embodiments of the present disclosure may include enabling apublisher to improve yield by charging more appropriate prices for thepublisher's inventory. There are two primary markets for sellinginventory in television: “upfront” and “scatter.” The upfront marketgenerally occurs each September and is a large event where new programsand premium media is sold in a short period of time. The scatter marketoccurs continuously throughout the year and involves any media that wasnot sold during the upfront market.

Price setting on scatter inventory may be similar to airline ticketprices. As demand for inventory goes up, price should also go up. As anair-date approaches, price may have to drop to ensure that a saleoccurs. If the inventory is left unsold then the publisher may loserevenue.

In trying to set an effective price for their media, the publisher maydesire to set a price for each advertiser which is low enough to clear,but which also is high enough that the publisher could generate areasonable yield. In setting the price, publishers may set a singleprice for their media in what is known as a “rate card.” The rate cardmay be published weekly, for example.

However, a publisher may desire to have a more dynamic rate card price.Knowledge of the advertiser, their interest in the media, and theirhistorical prices paid, may inform such dynamic pricing. Thus, thepublisher may avoid over-discounting on media which the advertiser wouldwant to buy. Likewise, the publisher may determine to lower the price ofthe media that is not ideal for an advertiser.

In order to provide such dynamic pricing, exemplary embodiments of thepresent disclosure may include providing a yield maximization model thatmay predict the expected clearing price CPM30(A_(j), M_(i)) based onadvertiser historical prices paid and relevance to the advertiser. Analgorithm for providing a yield maximization model is illustrated inFIG. 7.

A yield maximization model according to exemplary embodiments of thepresent disclosure may be provided according to the following equation(step 710):

CPM30(A _(j) ,M _(i))*=  [Equation 10]

Σw ₁·HistCPM30(m _(i))·HistDiscount  [Equation 11]

+Σw ₂·CPM30(m _(i))  [Equation 12]

+Σ_(m) _(i) w ₃·HistCPM30(A _(j) ,m _(i))·HistDiscount(A_(j))  [Equation 13]

+Σw ₄·CPM30(A _(j) ,m _(i))  [Equation 14]

+Σ_(m) _(k) w ₅ ·|tratio(A _(j) ,m _(i))−tratio(A _(k) ,m _(k))|·CPM30(A_(j) ,m _(k))  [Equation 15]

+Σw ₆·CoView(m _(i) ,m _(k))·CPM30(A _(j) ,m _(k))  [Equation 16]

In Equation 10, W₁-W₆ are predetermined numerical weights.

Equation 11 may represent the historical price, such as from SQAD,Standard Media Index, and others—for a media pattern HistCPM30(m_(i))that matches the inventory that is being priced M_(i) adjusted by anoverall historical adjustment. For example, if M_(i) is CNN on Tuesdayat 8 pm is to be priced, then m_(i) of CNN on a weekday in prime timewould match.

Equation 12 may represent the historical clearing price for this mediapattern CPM30(m_(i)).

Equation 13 may represent the HistCPM30(A_(j), m_(i)) that theadvertiser has logged for patterns of media m_(i) that match theinventory being priced M_(i), scaled by the typical percentage offhistorical price that this advertiser historically achievesHistDiscount(A_(j)).

Equation 14 may represent the historical actual clearing price for apattern of media that has similar tratio or audience composition, scaledby the similarity.

Equation 15 may represent the historical actual clearing price for apattern of media that has similar a high probability of having the sameset top box persons viewing the pattern as the media being priced

Equation 16 may represent the historical actual clearing price for apattern of media that matches the inventory being priced.

Exemplary embodiments of the present disclosure may train the abovemodel on historical observations of inventory M, advertiser A, SQADprice SQADCPM30, and actual clearing price CPM30.

VI. Yield Maximization: Advertiser Win Probability Landscape/NegotiationSupport Tool

After building the yield maximization model above, exemplary embodimentsof the present disclosure may include estimating whether the advertiseris likely to accept a price which is above or below their expectedclearing price (step 720). Based on such an estimate, the publisher mayuse this to inform their negotiation strategy. For example, if theon-air date is approaching, then the publisher may desire to sell theirinventory rather than have it go unsold. In such a circumstance, thepublisher may, for example, use the yield maximization model provided byexemplary embodiments of the present disclosure to determine that theyshould drop their price so as to increase their win probability with theadvertiser. The yield maximization model provided by exemplaryembodiments of the present disclosure may thus enable the publisher topossibly avoid over-discounting their inventory by allowing thepublisher to determine that an advertiser is likely to buy the inventoryat the reduced price. In order to calculate this win probabilitylandscape, exemplary embodiments of the present disclosure may includemeasuring the empirical probability of win versus difference fromexpected (step 730), for example, according to the equation:

$\begin{matrix}{{P{r\left( Z \middle| {{x \cdot {CPM}}\; 30\left( {A_{j},M_{i}} \right)^{*}} \right)}} = {{\frac{occ}{N}\text{:}\mspace{14mu} {CPM}\; 30\left( {A_{j},M_{i}} \right)} \geq {{x \cdot {CPM}}\; 30\left( {A_{j},M_{i}} \right)^{*}}}} & \left\lbrack {{Equation}\mspace{14mu} 17} \right\rbrack\end{matrix}$

where occ is the number of observations of an advertiser paying morethan x·CPM30(A_(j), M_(i))* and N is the total number of observations.

VII. Sell-Side Optimizer: GUI Implementation

Exemplary embodiments of the present disclosure may further includeproviding a graphical user interface (GUI) to enable a network to view alist of advertisers that may be inserted against their inventory, asshown in FIG. 14.

The GUI may be organized into a grid which has TV inventory (1430)running down the page, and candidate advertisers who could be insertedagainst media inventory running across the page (1450). A series oflinked filters may be available on the left and right-hand panes (1420).The GUI may support two-dimensional sorting. Vertical sorting may enablethe sorting by, for example, schedule, cost of media, gain in relevance,relevance, or units available, etc. (1410), so that a network mayquickly review which inventory to address. Horizontal sorting may enablesorting by advertisers who could be inserted into each position (1450)by, for example, tratio relevance, or other metrics. The GUI may furtherprovide the current or most request ad for each media (1440).

VIII. Sell-Side Optimizer: Screenshots Showing Example Use Cases

The Graphical User Interface (GUI) provided by exemplary embodiments ofthe present disclosure may be used to achieve a number of objectives asdescribed below:

A. Best Media for an Advertiser

FIG. 19 shows a report that may be generated through the GUI provided byexemplary embodiments of the present disclosure relating to an AmericanBroadcasting Corporation (ABC) Saturday schedule. A network may use sucha report to determine the best inventory to recommend for an SUVadvertiser.

In FIG. 19, the relevance of each advertiser may be indicated byproviding tratios alone or in combination with additional visualinformation. The additional visual information may include, for example,icons and the coloring or shading of grid cells. As shown in FIG. 19, anSUV advertiser would reach few buyers in “The Chew” or “GeneralHospital,” (1 pm-2 pm Saturday) or even “Shark Tank” or “Last ManStanding” (8 pm-10 pm). However they would do well in College Footballfrom 3 pm-6 pm. Such a report may enable the advertiser to fine-tunetheir rotation boundaries to include football but avoid the otherprogramming.

B. Advertisers to Contact Via Grid GUI

The GUI provided by exemplary embodiments of the present disclosure maybe used to gain insight into the list of advertisers who may beinterested in a particular media placement.

FIG. 11 shows a report generated for Discovery Channel's Animal Planet.The report in FIG. 11 was generated by sorting by schedule (verticalsort), and advertiser tRatio descending (horizontal sort). As shown inFIG. 11, the ad relevance of each advertiser may be indicated byproviding tratios alone or in combination with additional visualinformation. The additional visual information may include, for example,icons and the coloring or shading of grid cells.

As shown in FIG. 11, in the early morning, “Music players” would be bestto insert since that appeals to young people, and young people tend tobe up in the early mornings. If that advertiser could not participate,then “Online Education,” “Auto,” and “Trucks” would be next in order ofrelevance and so would be next to contact to sell the inventory.

As shown in FIG. 11, there may be changes in recommended advertisers dueto the time of day. For example, “Fitness” becomes the most relevant adat about 6 AM, and then “Senior Life Insurance” around 11 AM and noon.The shift to “Senior Life Insurance” may relate to daytime viewingaudience being more predominantly elderly. The report further indicatesthat if no interested advertiser could be found for “Senior LifeInsurance” then the next advertisers of interest in order would be“Family Life Insurance,” “Term Life Insurance,” and “Dental Insurance,”which are all products that may be favored by advertisers targeting moremature viewers.

FIGS. 15-17 show another report generating GUI that may be provided byexemplary embodiments of the present disclosure to generate anadvertiser contact list.

The GUI shown in FIGS. 15-17 may allow a user to select media via adrop-down to select, for example, the network, day, and hour. The GUImay then return a list of the advertisers who may be interested in thismedia, and whom could be contacted to sell the inventory. Theinformation that may be provided for each advertiser may include theirhistorical spend and the agency who is managing their inventory. Inaddition, the information that may be provided for each advertiser mayinclude an expected clearing price which is the price at which thelisted advertiser may be willing to purchase the media placement.

In FIG. 15, tratio is the match between advertiser's population and theaudience of the program (1510). Music players and services are shown asthe top advertisers on MTV due to the demographics, while colleges andonline education would also be interested in purchasing TV spots (1540).This may be attributed to a predicted 9.09 clearance price for the musiccompanies and a predicted 8.82 clearance price for the technicalcolleges (1530). In addition, the report shows how much each agency hashistorically spent (1520). This can be useful for looking for agenciesthat are likely to buy in the future.

In FIG. 16, second-hand clothing sellers, interior decoration/homefurnishing, and education may be among the advertisers predicted to beinterested in buying the media placement. In addition, for advertiserswho do not have a good audience match (i.e., a low tratio), exemplaryembodiments of the present disclosure may offer prices that are lowerthan the historical CPM clearance rate.

Finally, in FIG. 17, “Life Insurance” and “Luxury Autos” are indicatedtop advertisers who would be interested in this inventory (1710).Furthermore, exemplary embodiments of the present disclosure mayindicate that that AARP may pay more because the media is very welltargeted to their audience (1720). For each advertiser, the report mayprovide an agency to contact that is the entity executing the buys onbehalf of the advertiser.

C. Schedule Improvements to Increase Ad Relevance

FIGS. 8 and 9 depict an example of a TV schedule for Music Television(MTV) such as may be provided by exemplary embodiments of the presentdisclosure. In FIGS. 8 and 9, the tratio of each ad may be indicated byproviding the tratios alone or in combination with additional visualinformation. The additional visual information may include, for example,coloring or shading of grid cells.

As shown in FIGS. 8 and 9, currently “Cosmetics” industry ads are beingaired against “Ridiculousness” in the early morning hours includingmidnight and 5 am. The relevance scores shown in FIGS. 8 and 9 indicatethat “Cosmetics” scores a tratio as low as 0.092 in these slots, whereasthe highest tratio ad industry is “Music.” “Cosmetics” may score poorlyfor these programs because Ridiculousness appeals primarily to youngmales, but does not appeal to young females. Instead, as shown in FIGS.8 and 9, “Music,” with a tratio as high as 0.493 in these slots, wouldbe a more relevant ad for this audience.

As shown in FIGS. 8 and 9, MTV is also airing “Charity” ads for themovie “Step Up.” However, the relevance scores shown in FIGS. 8 and 9indicate that “Education online” would be a better ad to insert. Here,the buyers of “Education online” are predominantly young and female,thus better matching the viewers of “Step Up.”

FIGS. 10A and 10B depict the same MTV schedule but in a grid view suchas may be provided by exemplary embodiments of the present disclosure.Here, exemplary embodiments of the present disclosure may show multipleadvertisers, and the ad relevance of each advertiser may be indicated byproviding tratios alone or in combination with additional visualinformation. The additional visual information may include, for example,coloring or shading of grid cells. The additional visual information maymake it possible to discriminate “blocks” and “striations” of color orshading in which ad relevance tends to follow the programs that arebeing aired. For example, in example shown in FIGS. 10A and 10B,“Ridiculousness” may appeal to one set of viewers, and “16 and Pregnant”to another. Different ads may be relevant for each program.

D. Largest Gains in Ad Relevance

FIG. 18 shows a TV schedule for WGN such as may be provided by exemplaryembodiments of the present disclosure. As shown in FIG. 18, the reportprovided by exemplary embodiments of the present disclosure may besorted in order of largest gains in relevance. As shown in FIG. 18, thead relevance of each advertiser may be indicated by providing tratiosalone or in combination with additional visual information. Theadditional visual information may include, for example, icons and thecoloring or shading of grid cells.

As shown in FIG. 18, after sorting by the largest increase in relevance,the report indicates that the largest potential gain would be byreplacing “Online Education” ads against WGN News at Nine. The WGN Newstends to be viewed by an older audience, and so “Online Education” maybe poor match since it appealed to mainly young people. Instead a “Powertools” ad would have been a better choice to air with WGN News at Nine.

The next highest relevance improvement, as shown in FIG. 18, would befrom optimizing the ad for Futurama at 3 am. At this time in themorning, the only people watching tend to be very young. In addition,Futurama is a program that is viewed by younger people. Yet a ColonialPenn Life Insurance ad was the one most frequently run. As shown in FIG.18, a “Music Player” ad may have been more effective in this slot.

In addition, FIG. 12 shows another report that may be provided byexemplary embodiments of the present disclosure, here showing possiblead insertions by tratio difference between current advertisement andoptimal descending. For example, on Animal Planet, “Dogs 101” airs“Mitsubishi Outlander trucks” most frequently. However a better ad toinsert would be “Fitness” (the heart-shaped icon). “Dogs 101” appeals toyounger females, and so truck ads have poor relevance for this audience.

FIG. 13 shows another report that may be provided by exemplaryembodiments of the present disclosure, here showing the relevance forone particular advertiser (weight-loss). This report indicates that theaudience for “Dogs 101” may be a close match to the people who purchaseweight-loss products, as might “Too Cute!” However “Gator Boys” may notbe viewed by a relevant audience and so this ad may not be irrelevant toviewers of that program.

In FIGS. 12 and 13, the ad relevance of each advertiser may be indicatedby providing tratios alone or in combination with additional visualinformation. The additional visual information may include, for example,icons and the coloring or shading of grid cells.

Other embodiments of the disclosure will be apparent to those skilled inthe art from consideration of the specification and practice of theinvention disclosed herein. It is intended that the specification andexamples be considered as exemplary only, with a true scope and spiritof the invention being indicated by the following claims.

IX. Glossary of Terms and Calculations

The following section will provide more detail about the various termsand calculations used throughout this disclosure. These include termssuch as tratio, CPM, CPM30, tratio_positive, tCPM30, and others. It alsoprovides more detail about how industries are defined including anexample industry.

tratio

tratio measures how well targeted are advertisements. For example, let'ssay that one advertiser is placing “Power tools” ads on CWs “VampireDiaries.” Vampire Diaries is viewed by younger, female audience,where-as power tools are purchased by older males. This ad placement isintuitively poorly targeted—the ad product and the viewing populationare completely different. In contrast, let's say that another advertiserplaced their “Power Tools” ad on HIST's “Top Gear.” The audience viewingTop Gear tend to be male, older, handymen. The “Power Tools” productwould hit a lot more targets per impression. tratio is a per impressionmeasure. A well-targeted ad would hit a potential buyer everyimpression. Of course in practice it is more common to reach a potentialbuyer every thousand impressions or more. Because it is a per impressionmeasure, this means that shows that have smaller audiences are notpenalized. For example, one could try to target “American Idol” to reachhandymen. However, in order to reach those Handymen, the advertiser ishaving to buy millions of impressions of people that are not in theright target. Therefore, we find it useful to have a per impressionmeasure that indicates buyer concentration. This simply indicates howrich each program is in terms of the buyer concentration. In many ways,rather than paying for impressions, advertisers could essentially payfor buyers, and the buyers per million simply conveys the value perimpression of the media.

tratio can be calculated several ways, however a simple definition thatwe can use in one embodiment is it is the correlation coefficientbetween the demographic vector of purchaser demographics and audiencedemographics in a media program. Tratio is a number from −1 . . . 1. Thecorrelation coefficient measures how many sums of squares in the shapeof the target vector, are matched by the media vector. The criticalbenefit of this metric is that it is UNIVERSAL, GLOBAL, and COMPARABLEbetween advertisers, industries, and other factors. Under this scheme, a+0.5 in one industry means a certain amount of sums of squares accountedfor between the target and media vector. In another industry, a +0.5 maybe achieved with different variable values, but means the same thing interms of how good is the match. It does not need to take into accountthe number of buyers, which vary from one product to another, and varyin terms of the potential universe of buyers, or the brand'seffectiveness in advertising to-date.

The tratio is an absolute scale, normalized number from −1 . . . 1.Three cases are of special interest:

-   -   a. +1 indicates that the media being purchased is a perfect        match for the target.    -   b. −1 indicates that the media being purchased is the opposite        of what the advertiser should be purchasing. For example, say        that the advertiser's product is “senior life insurance,” which        is life insurance that seniors purchase late in life to defray        burial costs. A −1 could occur if the advertiser is targeting        Cartoon network or MTV which are viewed by very young people,        when in fact they should be targeting elderly people.    -   c. 0 Indicates that the media being purchased is effectively        random. Because of the normalization regime, 0 means that the        vector is effectively a match for the average of US population.

Because of the universality of the metric, we can use it to report onoverall TV targetedness, and compare different industries to see howtheir natural levels of targeting vary.

We will talk briefly about TRPs and how they are different from tratio.Age-gender Target Rating Points (TRPs) are a traditional method formeasuring targeting on television. This works by counting the number ofpersons with the desired age-gender and dividing by total population.For example if the target was females 25 to 54, we would count thenumber of persons who were female and 25 to 54 and then divide bypopulation. However TRPs have several limitations that effectively meanthat they are a bad fit when working with Set Top Box data.

-   -   a. The most problematic issue for TRPs is that the definition of        the actual target is not known (e.g. how did we come up with        “females 25 to 54”? TRPs are agnostic on exactly how the target        is defined; in contrast the STB methods we use will        automatically identify the target).    -   b. A second issue is age-gender TRPs use very few variables        (i.e. just age and gender). STB methods are able to use        thousands of variables for matching.    -   c. A third issue is that these are a conjunctive expression        (e.g. Female AND “25 to 54”), and measure a target as either in        or out (If you're “Female” AND “25 to 54” we score you as a 1        and 0 otherwise). This may be okay when using a small number of        variables such as age and gender, but causes problems when        working with high-dimensional data. Already with just age and        gender, the total population drops from 100% to about 1%.        Imagine what would happen if we add a third variable? Or a        fourth? The number of persons matching on all of these        demographics quickly drops to almost zero. It is possible to        have thousands of descriptive demographic variables including        “diabetes interest”, “NASCAR spectator sports interest”,        “occupation=self-employed blue collar”, and so on. If we create        a Boolean and expression with all of these variables, we'll find        that almost nobody is left who will match on exactly every        variable. In order to work in practice, we need to use a method        that is able to handle these high-dimensional spaces. Therefore        we have to move from a hard in/out definition, to one which uses        similarity, and then can utilize thousands of comparison        measurements.

tratio_positive

In order to use tratio for many graphs and analyses, we will use a morerobust measure called tratio_positive. This is defined as follows:

Tratio_positive=max(tratio,0.05)

tCPM_positive=CPM/tratio_positive

This measure excludes negatively targeted media from consideration, andfocuses on positively targeted media. We will tend to usetratio_positive in most analyses because it is more robust and allows usto work with a positive number. For example, an advertiser might have anaverage tratio of −0.20. However, they may have advertised some media at0.10 and 0.20. We ignore the negatively targeted airings set those to0.05. The resulting score focuses on the positive airings.

Cost or Spot Price

Spot prices are the prices that advertisers pay to advertise theirmedia. Because advertisers use different media lengths (15 second, 30second, 60 second and 2 minute ads), we “equivalize” the media to theprice of an equivalent 30 second standard advertisement (30 second isthe most common ad length). This is calculated by takingCPM30=CPM/(30/medialength) and we refer to this as the “Equivalized CostPer Thousand” or CPM30. For example, if an advertiser has a 60 second adand it cost $1000, then the 30 second equivalent ad would have aspotcost of $500.

CPM

Cost per thousand impressions. This is often used when referring totelevision advertising prices.

CPM30

Different advertisers use different lengths of advertisements. Someadvertisers use 15 second, 30 second, 60 second and 120 secondadvertisements. We have found that generally the cost of these adsscales linearly with the number of ad seconds. In order to produce ameasurement of CPMs, we have to standardize to a particular medialength. We do that by setting 30 second ads as our standard.

CPM30=CPM/(medialength/30)

Every airing in the United States is tracked, and an estimate of itsclearing price is made. That estimate is often referred to as the “ratecard rate”. We surface these rate card rates, along with Nielsen quotedimpressions, for every airing. This gives us the Cost and Impressions.Finally, we then apply our targeting measure of buyers per million.

tCPM30

tCPM30 is the cost per targeted impression, and indicates how costeffective is a targeted TV campaign. Lower tCPM30 indicates better valueper dollar. Higher tCPM30 means worse value per dollar. tCPM30 iscalculated as

CPM30/tratio

Star Ratings

Star ratings are a convenient notation for indicating how good or bad aparticular campaign is performing. In general, star ratings are a 5 starscale, where each star is equal to a 20^(th) percentile. For example, anadvertiser with 1 star means that they are performing in the lower20^(th) percentile of the group of comparable advertisers. An advertiserwith 5 stars indicates that they are performing in the 80^(th)percentile.

In order to create star ratings that go across industries, we have totake into account that each industry has different tratios and tCPMs. Ineach industry the agency's tCPM is compared against the average for theindustry, and converted to standardized units of how much higher orlower they are from the industry average.

For example, if the mean tCPM for the industry were 20, standarddeviation was 10, and the agency had a tCPM of 10, then we would convertthe agency's performance into standardized units of −1.0; meaning thatthe agency was executing a tCPM that was 1 standard deviation below thenorm for the industry. We calculate these standardized discrepancies foreach industry that an agency participates in. We then average thediscrepancies to give a final standardized score. Let's say that theagency received standardized scores of +0.5, −1.0, −1.5 in threeindustries—we then average those to produce −1.0. The final step forstar ratings is that the stars are assigned based on the rank of theagency compared to all other agencies. Let's say that there were 50agencies, and the agency in question scored −1.0, and this meant that itwas the 8^(th) best agency out of 50. We then would assign the company 5stars out of 5, since it is ranked ahead >40 of the agencies—i.e. it isin the top 20^(th) percentile.

Upper and Lower Bounds

Upper and lowers show the upper 20^(th) percentile and lower 80^(th)percentile values. These are calculated by (a) summarizingagency-advertiser performance for a given day as an average tratio,tCPM, CPM, and so on, (b) taking a centered moving average for 90 daysprior and 90 days after the current day, (c) reading off the 20^(th) and80^(th) percentile for values over this period of time.

Moving Averages

Most timeseries are centered moving averages. Typically tratio, tCPM andCPM measurements are summarized to averages for the day, and then theseare blended with the tratio, tCPM and CPM measurements for 90 days priorand 90 days after the current day. We use moving averages becauseadvertisers typically go on and off the air, and we want to build up ageneral picture of the behavior of the advertisers.

Methods for Handling Low Data Regions

There are situations in which there may be very little data for aparticular agency-advertiser. For example, the centered moving averagetakes 90 days before and 90 days after the present. However, let's saythat we are approaching the end of the timeseries, and we may have 20days before present, and 0 days after. When that occurs, the system willautomatically exclude this data point because of low data availability.

Industry Definition Details

Industry Definition

A wide variety of companies advertise on television. However there arebig differences between financial services and exercise equipmentcompanies—different people buy these products and so advertisers need togo after different TV media. In order to measure their targeting andCPMs, we need to be able to segment TV advertisers into different groupsor industries. We define the following for each industry:

II. Product

This is the product that the companies in this space. This ranges fromlife insurance, to power tools, to air travel. An example set ofindustries are defined in table A. This is an excerpt from a tablecalled Advertiser.Advertiser, and each Advertiser defined here (withJobID as the primary key) represents a “collection” of airings thatbelong to a variety of companies.

TABLE A Advertiser.Advertiser table showing several industries that havebeen defined. Airing Source Advertiser Airings Airings Primary CountJobID Advertiser Name Key Type To Pull available Processing LocalPanelID Processing 1 Charity 110382 Industry 100000 497455 0 0 11 0 2Diabetic Health 110462 Industry 100000 NULL 0 0 11 0 insurance 3 Diet110417 Industry 100000 257311 0 0 11 0 4 Dental Insurance 110406Industry 100000 NULL 0 0 11 0 5 Home Furnishings 110401 Industry 100000264864 0 0 11 0 6 Investment 110528 Industry 100000 711295 0 0 11 0Services 7 Life Insurance 110402 Industry 100000 229197 0 0 11 0 8 Music110254 Industry 1000000 35023 0 0 11 0 9 Power tools 10023 Industry100000 107493 0 0 11 0 10 SUVs 110347 Industry 100000 977988 0 0 11 0

III. Target Buyer Population

This is a set of persons who have bought the product in question. Forexample, if life insurance is the industry, the persons purchased a lifeinsurance policy. After we create the target buyer population, we canreport on the demographics of that population and where they are on TV.

In our database schema we represent these collections of productpurchasers under a key called a sourcekey. This is a unique identifierthat refers to the population of buyers. Table B shows an example ofproduct purchasers unified under a particular sourcekey.

TABLE B PersonSource table. Product purchasers (persons) identified asbelonging to a particular sourcekey. Source Person Create Churn CustomerKey Key Date Acguisition Date Date ID 110424 10532089 14:22.1 12/29/1112:00 AM NULL a 110424 10532146 26:15.1 2/14/98 12:00 AM NULL b 11042410532158 14:22.1 4/20/13 12:00 AM NULL c 110424 10532187 26:15.1 6/10/0412:00 AM NULL d 110424 10532395 14:22.1 12/29/11 12:00 AM NULL e 11042410532513 14:22.1 1/3/12 12:00 AM NULL f 110424 10532573 26:15.1 2/14/9812:00 AM NULL g 110424 10532580 14:22.1 2/29/12 12:00 AM NULL h 11042410532674 26:15.1 2/14/98 12:00 AM NULL i 110424 10532713 14:22.1 8/27/1212:00 AM NULL j

TABLE C Person Table. The persons are anonymous. Person State/ Zip/ KeyName City Province PostalCode 1 1 Greeley CO 80634 2 25 Cottontown TN37048 3 32 COLUMBIA SC 29209 4 411 Pine Hill NJ 08021 5 51 ALMIRA WA99103 6 6333 MONTICELLO KY 42633 7 74 FALL CREEK WI 54742 8 82PHILADELPHIA PA 19111 9 91 MILLEN GA 30442 10 10 Beaumont TX 77706

TABLE D Demographics Table. This is a list of demographics supported bythe system. DemographicsID Demographics Name 23 Allergy Related Interest24 Arthritis, Mobility Interest 25 Health - Cholesterol Focus 26Diabetic Interest 27 Health - Disabled Interest 28 Orthopedic Interest29 Senior Needs Interest 30 PC Internet Connection Type 31 Single Parent32 Veteran 33 Occupation - Professional

TABLE E DemographicsValue table. This is a list of variables for eachdemographic. For example, for something like “age = 18to20”, “age” isthe demographic, and “18to20” is the demographicvalue. DemographicsValue Demographics Demographics Value ID ID Name 91 30 Cable Internet 9230 DSL Internet 93 30 Dial-Up Internet 96 33 Occupation - Professional97 33 Architect 98 33 Chemist 99 33 Curator 100 33 Engineer 101 33Aerospace Engineer 102 33 Chemical Engineer

TABLE F PersonDemographicsMap Table. This notes a demographic trait thateach person has. Person Demographics Demographics Key ID Value ID10174988 14 76 10174988 44 55953 10174988 47 539 10174988 58 5597210174988 63 660 10174988 65 662 10174988 84 681 10174988 101 69510174988 116 712 10174988 134 764

TABLE G SourceVariableValueProfile. The above table is an aggregation ofthe product purchaser demographics, and for each demographic-demographicvalue, it calculates a percentage of the time that the traitexists in the population defined by sourcekey, and then translates thatinto a z-score to give a measure of how unusual this percentage iscompared to the US population. Demographics Demographics Value SourceKey ID ID ZScore 10021 93 56025 0.683761948 10021 93 56026 0.96843915910021 93 56027 0.204178975 10021 93 56028 0.80541485 10021 93 56029−0.208246564 10021 93 56030 0.62596653 10021 93 56031 −0.038819315 1002193 56032 0.11029697 10021 94 56033 0.013483197 10021 94 560340.267608507

The Set of Companies Participating in this Industry

The products being sold by the advertisers in this industry are oftendirect competitors. For example, both American Express Gold Card andChase Sapphire offer Premium, yearly fee credit cards; one at a pricepoint of $165 and the other at $185. These two companies both belong toa premium credit card industry. Power tools companies such as Makita,Boch, etc., are ideally trying to reach amateur handymen andcontractors, and belong to the “Power Tools” industry. BMW and Mercedesboth sell luxury autos with a similar buying population profile. Ingeneral we have made available industries which have clear targetprofiles, and where we can have confidence that the products being soldare similar enough to compare in this manner.

We next identify competitor companies by looking for a NielsenProductthat matches the target population. For example, for the Power Toolsindustry, NielsenProductName=‘Power Tools-Access’ properly identifiesall of the “power tool ads”. We then recover the NielsenDivisionNamesassociated with those ad airings, and we end up with a list of companiesincluding Boch, Positec, Makita and so on.

In most cases, the NielsenProduct and NielsenDivisionNames is sufficientto properly identify the companies selling a particular product. Howeverin some cases we have to create exclusions to avoid picking up somecompanies that are selling different products to the rest of theindustry. In general our product definitions follow Nielsen and caseswhere have to implement exclusions are not as common. Detailedinformation on the definitions for every industry are below.

TABLE H Nielsen product hierarchy table Nielsen Product Nielsen ProductHierarchy ID Hierarchy Name Nielsen Ad Occurrence Column Name 1 IndustryNielsenProductIndustryCategoryName 2 Major CategoryNielsenProductMajorCategoryName 3 Sub-Group CategoryNielsenProductCategoryName 4 Parent Company NielsenCompanyName 5 ProductCategory NielsenProductName 6 Subsidiary NielsenDivisionName 7 BrandNielsenBrandName 8 Brand Variant NULL 9 Creative NULL

In terms of technical implementation, we actually decompose all of theabove NielsenProduct definitions into Nielsen's lowest levelclassification which is NielsenBrandName, and we use the collection ofNielsenBrandNames to identify all airings belonging to the companies inthe industry.

The database schema which represents industry definitions is below:

TABLE I Identifier mappings to each iobid (industry). For example,“Weight Loss Program” found at Nielsen hierarchy level 4(NielsenCompanyName) maps to 3 which is an advertiser industry for“Diet”. The above table also shows a special “exclude” directive - whenexclude = 1, instead of including when the above string is detected atthe appropriate level of the hierarchy, any airings are excluded.PanelID refers to the “airing source” which is the source of the airingsthat are being sampled. For example, Nielsen Monitorplus may be 11, andBVS Verified airings may be 9. Civolution may be 13. Job Panel NielsenProduct ID Identifier Name ID Hierarchy ID Exclude 1 Charitable Orgn 6 50 1 Charitable Orgn 11 5 0 3 EDIETS.COM INC 6 4 1 3 EDIETS.COM INC 11 41 3 NUTRI/SYSTEM INC 6 4 1 3 NUTRI/SYSTEM INC 11 4 1 3 PERSONALENHANCEMENT 6 4 1 & NUTRITION 3 PERSONAL ENHANCEMENT 11 4 1 & NUTRITION3 RODALE INC 6 4 1 3 RODALE INC 11 4 1 3 THIN FOR LIFE-3L 6 4 1 3 THINFOR LIFE-3L 11 4 1 3 Weight Loss Program 6 5 0 3 Weight Loss Program 115 0 9 Power Tools-Access 6 5 0 9 Power Tools-Access 11 5 0

TABLE J columns 1-4 AiringID SourceSegmentKey JobID ProgramName888006999 10023 9 PARDON THE INTERRUPTION 888007000 10023 9 SPEEDERS888007001 10023 9 GHOST ADVENTURES 888007002 10023 9 TSG PRESENTS888007003 10023 9 AMER FUNNIEST HOME VIDEOS 888007004 10023 9 NFL TOTALACCESS 888007005 10023 9 MONSTERS AND MYSTERIES IN 888007006 10023 9NASCAR NOW 888007007 10023 9 NASCAR NOW L 888007008 10023 9 WORLDSDEADLIEST AIRCRAFT

TABLE J columns 5-11 Hour Day AirDate TRatio Impressions Cost CPM of Dayof Week 3/26/10 6:11 PM 0.190413 178082 1229.5137 6.9042 18 6 3/26/103:38 AM −0.04392 159304 308.3488 1.9356 3 6 3/25/10 10:51 PM −0.08661236204 1184.9174 5.0165 22 5 3/25/10 10:20 PM −0.00398 395340 1189.54843.00893 22 5 3/25/10 9:31 PM 0.159927 92704 192.2588 2.0739 21 5 3/26/1010:10 AM 0.009535 67577 272.3167 4.02973 10 6 3/26/10 12:09 AM 0.117099440801 3069.1651 6.9627 0 6 3/30/10 2:13 AM 0.204627 141522 746.58875.27543 2 3 4/20/10 5:24 PM 0.196591 141522 748.8177 5.29117 17 34/20/10 9:07 AM 0.373725 82770 158.0679 1.90972 9 3

TABLE J columns 12-19: Each record of the above table represent atelevision airing. The television airing was sampled for jobid = 9 inthe example above (Power Tools). The airing includes the datetime,program name (program mastered), station that it aired on (stationmastered), pod position in which it aired, and so on. The abovetelevision spots are then analyzed for their audience, and a relevancescore is calculated based on the match between the advertiser's productpurchasers and the audience of the airings above. Pod Pod Max Pod MarketStation Program Match Panel Group Position Position Master ID Master IDMaster ID Failure Airing ID Number Number Number 169 97 2315 0 5904169 13 5 169 752 59624 0 5903413 1 2 7 169 751 15812 0 3656474 5 4 6 169 752116878 0 5907195 2 4 10 169 952 3492 0 5906317 3 6 7 169 684 2405 05904148 4 1 9 169 86 17865 0 5904162 1 1 4 169 97 2261 0 5904874 1 2 6169 97 2261 0 5906735 3 3 7 169 670 NULL 1 3420817 1 3 5

Example Industry 1: Power Tools Industry

Advertiser Definition

The Power Tools industry is defined as all NielsenDivisionName companiesthat are listed under NielsenProductName=‘Power Tools-Access’ by NielsenCorporation. 16 NielsenDivisionNames are listed ranging from SearsRoebuck & Co to Makita USA.

The products that are sold under these headings are shown in FIG. 2.There are 68 products defined ranging from DREMEL 400 SERIES XPR POWERTOOLS-ACCESS to ROCKWELL BLADERUNNER POWER TOOLS-ACCESS.

Buyer Target Definition

Let us assume that we have 112,233 persons who have bought power tools,ranging from oscillating tools, to cutting tools, drills, and workbenchstands. The product counts are shown below

TABLE K Power tools buyers that are being used for targeting sourcekeySourcecompanyname persons 10023 Saw stand 48421 10036 Oscillating tool20946 10084 Cutting tool 13668 110088 Oscillating tool 9839 110115Drills 5879 110116 Other General Handyman tools 13480 Total 112,233

TABLE L NielsenDivisions detected in the Power Tools industry JobIDNielsenDivisionName Cost CostRank Impressions 9 SEARS ROEBUCK & CO6448305 1 1124061344 9 POSITEC USA INC 6331959 2 1180631424 9 ROBERTBOSCH TOOL CORP 1258700 3 313894224 9 HOME DEPOT INC 802951 4 1454498089 ECHO INC 626752 5 89720848 9 RYOBI TOOLS INC 376469 6 63577136 9 BLACK& DECKER CORP 164736 7 30052512 9 MOTHERS POLISHES-WAXES- 153516 828104272 CLEANERS INC 9 STIHL INC 135574 9 24437504 9 OREGON CUTTINGSYSTEMS DV OF 97883 10 16301600 BT INC 9 FEIN POWER TOOLS INC 74861 1122792112 9 PORTER-CABLE CORP 59889 12 4954656 9 BSH HOME APPLIANCES CORP18269 13 5165104 9 MAKITA USA INC 5282 14 974400 9 LARRY HESS & ASSOCINC 4334 15 1176336 9 GUARDAIR CORP 770 16 146720

TABLE M Top Demographic variables for power tool buyers Variable d Valuev Z-score P_(d, v) ⁺ Off-Road Recreational Vehicles True 3.396183 DIYLiving True 1.787005 Personicx Classic Country Ways 1.748729 HomeImprovement - DIYer True 1.336364 Woodworking True 1.258405 Hunting True1.180173 Personicx Classic The Great Outdoors 1.167606 MilitaryMemorabilia, Weaponry True 1.107564 Personicx Classic Full Steaming1.080662 Personicx Classic Acred Couples 1.068328 Science, Space True1.01155 Personicx Classic Rural Retirement 1.008405 Motorcycling True0.956336 Auto Parts and Accessories True 0.911406 DOB - Year Bom in the1940s 0.908301 Motorcycle Owner True 0.903744 Crafts, Hobbies InterestTrue 0.870761

Example Industry 2: Charity Industry

Advertiser Definition

The Charity industry is defined as all companies that are advertisingwith brands that are within NielsenProductName=‘Charitable Orgn’.

TABLE N JobID NielsenDivisionName Cost CostRank 1 MCDONALDS CORP 85827001 1 AMERICAN CANCER SOCIETY INC 8028839.5 2 1 AMERICAN SOCT PRVNT CRLTYANMLS INC 5584664.9962671 3 1 CHILDFUND ALLIANCE 4175138.270236 4 1HUMANE SOCIETY OF THE US 2909849.9999254 5 1 JUVENILE DIABETES RESEARCHFNDN 2332250 6 1 TELETON USA 2239296 7 1 SALVATION ARMY 2197725.5 8 1UNITED WAY 1810961 9 1 CHILDREN INTL 1372539 10 1 ENTERTAINMENT INDUSTRYFNDN 1347846 11 1 ALZHEIMERS DISEASE RTD DSR ASSN INC 1139313 12 1PARTNERSHIP FOR A DRUG-FREE AMERICA 1118875.5 13 1 PRODUCE FOR BETTERHEALTH FNDN INC 1115152 14 1 AVON PRODUCTS INC 1021685 15 1 PARTNERSHIPAT DRUGFREE.ORG 999418 16 1 DKMS AMERICAS 997697 17 1 AMERICAN HEARTASSN INC 950747 18 1 IRAQ & AFGHANISTN VTRNS OF AMRC INC 907540.4 19 1CHURCH OF SCIENTOLOGY 827361 20

Buyer Target Definition

Targeting is using over 409,025 buyers who have donated to a children'scharity.

TABLE O Sourcekey SourceCompanyName Persons 110382 Children's charity409,025

The top demographics of this group of donors is that they are (a) age70+, (b) have high incomes, or a large amount of discretionary income,(c) are classified seniors, (d) female.

Top 20 demographic variable-values

TABLE P Variable Customer Index Value Demographics Name DemographicsDescription Sort Pct Vs Avg Count Luxury SUV - Most 02 2 0.1468585.727161 18054 Likely to Own Donation, True 1 0.356982 4.175142 48430Contribution DOB - Year Bom Before 1930 1 0.152037 3.62542 18472Discretionary Income Lower Discretionary 2 0.023693 3.257232 2910 IncomeIndex (15-29) Investing True 1 0.263502 3.132852 35748 Audio Books andTrue 1 0.021457 2.894037 2911 Music Personicx Classic Suburban Seniors28 0.059221 2.875299 7764 Health - Cholesterol True 1 0.11067 2.77213815014 Focus Young Men's Apparel True 1 0.025172 2.73263 3415 PetiteWomen's True 1 0.106579 2.729189 14459 Apparel Age 76+ 99 0.2599852.555719 31628 Personicx Classic Timeless Elders 66 0.019542 2.3161392562 Income Greater than $500K 13 0.024712 2.097285 3038 PersonicxClassic Devoted Duos 49 0.038382 2.068783 5032 Senior Needs True 10.023167 2.047854 3143 Interest Infants and Toddler True 1 0.0578481.89106 7848 Apparel Occupation - Legal/Attorney/Lawyer 15 0.0263311.662399 1075 Professional Income Range $400-500K 23 0.009111 1.6582481120 Premium Female 75+ True 1 0.223079 1.646957 30264 Income $300-500K12 0.017668 1.61112 2172

1-20. (canceled)
 21. A method of recommending television ad placementfor multiple advertisers, the method comprising: calculating, by theprocessor, a cost ratio for each program among the plurality of programsas a cost per targeted impression divided by a calculated firstrelevance of each advertising target among a plurality of advertisingtargets for the respective program; calculating, by the processor, asecond relevance of an advertising target associated with the respectiveprogram; and generating, by the processor, recommendations for anadvertising target among the plurality of advertising targets based onthe calculated first relevance of each advertising target, thecalculated second relevance of the associated advertising target, andthe calculated cost ratio.
 22. The method of claim 21, furthercomprising: calculating, by the processor, the first relevance based ona first correlation coefficient between demographic attributes ofproduct purchasers among the plurality of advertising targets anddemographic attributes of viewing persons viewing the program.
 23. Themethod of claim 21, wherein calculating the second relevance of theadvertising target associated with the respective program is based on asecond correlation coefficient between demographic attributes of theproduct purchasers among the plurality of advertising targets anddemographic attributes of the viewing persons viewing the respectiveprogram
 24. The method of claim 21, wherein generating therecommendations comprises sorting each program by the calculated costratio and selecting the program having the smallest calculated costratio as a target program.
 25. The method of claim 24, whereingenerating the recommendations further comprises sorting each identifiedprogram by the calculated first relevance of the advertising target andselecting the identified program having the greatest calculated firstrelevance as an additional target program.
 26. The method of claim 21,wherein generating the recommendations comprises sorting each advertiserindustry by the calculated first relevance of the identified program andselecting the advertising target having the greatest calculated firstrelevance as the advertising target.
 27. The method of claim 24, furthercomprising: identifying available programs among the identifiedprograms, wherein the generating recommendations further comprisessorting each identified available program by the calculated firstrelevance of the advertising and selecting the identified availableprogram having the greatest calculated first relevance as an additionaltarget program.
 28. The method of claim 24, further comprising: for eachidentified program, calculating an increased relevance as the differencebetween the calculated first relevance of advertising target for theidentified program and the calculated second relevance of the determinedadvertisement for the identified program, wherein generating therecommendations further comprises sorting the identified programs by thecalculated increased relevance and selecting the identified programhaving the greatest calculated increased relevance as an additionaltarget program.
 29. A system for recommending television ad placementfor multiple advertisers, the system comprising: a server providingprogram information for a respective identified program aired in eachmedia slot among a plurality of media slots over a network, the programinformation including viewing data of a plurality of viewing personsviewing the program; and an advertising targeting controller configuredto: calculate a cost ratio for each program among the plurality ofprograms as a cost per targeted impression divided by a calculated firstrelevance of each advertising target among a plurality of advertisingtargets for the respective program; calculate a second relevance of anadvertising target associated with the respective program; and generaterecommendations for an advertising target among the plurality ofadvertising targets based on the calculated first relevance of eachadvertising target, the calculated second relevance of the associatedadvertising target, and the calculated cost ratio.
 30. The system ofclaim 29, wherein the advertising targeting controller is furtherconfigured to: calculate, by the processor, the first relevance based ona first correlation coefficient between demographic attributes ofproduct purchasers among the plurality of advertising targets anddemographic attributes of viewing persons viewing the program, whereincalculating the second relevance of the advertising target associatedwith the respective program is based on a second correlation coefficientbetween demographic attributes of the product purchasers among theplurality of advertising targets and demographic attributes of theviewing persons viewing the respective program
 31. The system of claim29, wherein generating the recommendations comprises sorting eachprogram by the calculated cost ratio and selecting the program havingthe smallest calculated cost ratio as a target program.
 32. The systemof claim 29, wherein generating the recommendations comprises sortingeach advertiser industry by the calculated first relevance of theidentified program and selecting the advertising target having thegreatest calculated first relevance as the advertising target.
 33. Thesystem of claim 31, wherein the advertising targeting controller isfurther configured to: identify available programs among the identifiedprograms, and wherein generating the recommendations further comprisessorting each identified available program by the calculated firstrelevance of the advertising target and selecting the identifiedavailable program having the greatest calculated first relevance as anadditional target program.
 34. The system of claim 31, wherein theadvertising targeting controller is further configured to: for eachidentified program calculate an increased relevance as the differencebetween the calculated first relevance of advertising target for theidentified program and the calculated second relevance of the determinedadvertisement for the identified program, and wherein generating therecommendations further comprises sorting the identified programs by thecalculated increased relevance and selecting the identified programhaving the greatest calculated increased relevance as an additionaltarget program.
 35. A non-transitory computer readable medium storing aprogram causing a computer to execute a method of recommendingtelevision ad placement for multiple advertisers, the method comprising:calculating, by the processor, a cost ratio for each program among theplurality of programs as a cost per targeted impression divided by acalculated first relevance of each advertising target among a pluralityof advertising targets for the respective program; calculating, by theprocessor, a second relevance of an advertising target associated withthe respective program; and generating, by the processor,recommendations for an advertising target among the plurality ofadvertising targets based on the calculated first relevance of eachadvertising target, the calculated second relevance of the associatedadvertising target, and the calculated cost ratio.
 36. Thenon-transitory computer readable medium according to claim 35, theexecuted method further comprising: calculating, by the processor, thefirst relevance based on a first correlation coefficient betweendemographic attributes of product purchasers among the plurality ofadvertising targets and demographic attributes of viewing personsviewing the program, wherein calculating the second relevance of theadvertising target associated with the respective program is based on asecond correlation coefficient between demographic attributes of theproduct purchasers among the plurality of advertising targets anddemographic attributes of the viewing persons viewing the respectiveprogram
 37. The non-transitory computer readable medium according toclaim 35, wherein generating the recommendations comprises sorting eachprogram by the calculated cost ratio and selecting the program havingthe smallest calculated cost ratio as a target program.
 38. Thenon-transitory computer readable medium according to claim 37, whereingenerating the recommendations further comprises sorting each identifiedprogram by the calculated first relevance of the advertising target andselecting the identified program having the greatest calculated firstrelevance as an additional target program.
 39. The non-transitorycomputer readable medium according to claim 35, wherein generating therecommendations comprises sorting each advertising target by thecalculated first relevance of the identified program and selecting theadvertising target having the greatest calculated first relevance as theadvertising target.
 40. The non-transitory computer readable mediumaccording to claim 37, the executed method further comprising: for eachidentified program calculating an increased relevance as the differencebetween the calculated first relevance of advertising target for theidentified program and the calculated second relevance of the determinedadvertisement for the identified program, wherein generating therecommendations further comprises sorting the identified programs by thecalculated increased relevance and selecting the identified programhaving the greatest calculated increased relevance as an additionaltarget program.